Overview

Dataset statistics

Number of variables33
Number of observations201
Missing cells51
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.9 KiB
Average record size in memory264.6 B

Variable types

Numeric21
Categorical12

Alerts

symboling is highly correlated with normalized-losses and 3 other fieldsHigh correlation
normalized-losses is highly correlated with symbolingHigh correlation
wheel-base is highly correlated with symboling and 12 other fieldsHigh correlation
length is highly correlated with wheel-base and 14 other fieldsHigh correlation
width is highly correlated with wheel-base and 12 other fieldsHigh correlation
height is highly correlated with symboling and 4 other fieldsHigh correlation
curb-weight is highly correlated with wheel-base and 12 other fieldsHigh correlation
engine-size is highly correlated with wheel-base and 12 other fieldsHigh correlation
bore is highly correlated with wheel-base and 12 other fieldsHigh correlation
compression-ratio is highly correlated with diesel and 1 other fieldsHigh correlation
horsepower is highly correlated with length and 11 other fieldsHigh correlation
city-mpg is highly correlated with length and 11 other fieldsHigh correlation
highway-mpg is highly correlated with wheel-base and 12 other fieldsHigh correlation
price is highly correlated with wheel-base and 12 other fieldsHigh correlation
city-L/100km is highly correlated with length and 11 other fieldsHigh correlation
highway-L/100km is highly correlated with wheel-base and 12 other fieldsHigh correlation
diesel is highly correlated with compression-ratio and 1 other fieldsHigh correlation
gas is highly correlated with compression-ratio and 1 other fieldsHigh correlation
normalized_length is highly correlated with wheel-base and 14 other fieldsHigh correlation
normalized_width is highly correlated with wheel-base and 12 other fieldsHigh correlation
normalized_height is highly correlated with symboling and 4 other fieldsHigh correlation
symboling is highly correlated with normalized-losses and 3 other fieldsHigh correlation
normalized-losses is highly correlated with symbolingHigh correlation
wheel-base is highly correlated with symboling and 11 other fieldsHigh correlation
length is highly correlated with wheel-base and 12 other fieldsHigh correlation
width is highly correlated with wheel-base and 12 other fieldsHigh correlation
height is highly correlated with symboling and 2 other fieldsHigh correlation
curb-weight is highly correlated with wheel-base and 12 other fieldsHigh correlation
engine-size is highly correlated with wheel-base and 12 other fieldsHigh correlation
bore is highly correlated with length and 11 other fieldsHigh correlation
compression-ratio is highly correlated with diesel and 1 other fieldsHigh correlation
horsepower is highly correlated with length and 11 other fieldsHigh correlation
city-mpg is highly correlated with length and 11 other fieldsHigh correlation
highway-mpg is highly correlated with wheel-base and 12 other fieldsHigh correlation
price is highly correlated with wheel-base and 12 other fieldsHigh correlation
city-L/100km is highly correlated with length and 11 other fieldsHigh correlation
highway-L/100km is highly correlated with wheel-base and 12 other fieldsHigh correlation
diesel is highly correlated with compression-ratio and 1 other fieldsHigh correlation
gas is highly correlated with compression-ratio and 1 other fieldsHigh correlation
normalized_length is highly correlated with wheel-base and 12 other fieldsHigh correlation
normalized_width is highly correlated with wheel-base and 12 other fieldsHigh correlation
normalized_height is highly correlated with symboling and 2 other fieldsHigh correlation
wheel-base is highly correlated with length and 6 other fieldsHigh correlation
length is highly correlated with wheel-base and 11 other fieldsHigh correlation
width is highly correlated with wheel-base and 11 other fieldsHigh correlation
height is highly correlated with normalized_heightHigh correlation
curb-weight is highly correlated with wheel-base and 12 other fieldsHigh correlation
engine-size is highly correlated with wheel-base and 12 other fieldsHigh correlation
bore is highly correlated with length and 3 other fieldsHigh correlation
horsepower is highly correlated with width and 8 other fieldsHigh correlation
city-mpg is highly correlated with length and 10 other fieldsHigh correlation
highway-mpg is highly correlated with length and 10 other fieldsHigh correlation
price is highly correlated with wheel-base and 11 other fieldsHigh correlation
city-L/100km is highly correlated with length and 10 other fieldsHigh correlation
highway-L/100km is highly correlated with length and 10 other fieldsHigh correlation
diesel is highly correlated with gasHigh correlation
gas is highly correlated with dieselHigh correlation
normalized_length is highly correlated with wheel-base and 11 other fieldsHigh correlation
normalized_width is highly correlated with wheel-base and 11 other fieldsHigh correlation
normalized_height is highly correlated with heightHigh correlation
diesel is highly correlated with fuel-system and 1 other fieldsHigh correlation
fuel-system is highly correlated with diesel and 3 other fieldsHigh correlation
gas is highly correlated with diesel and 1 other fieldsHigh correlation
num-of-cylinders is highly correlated with make and 2 other fieldsHigh correlation
make is highly correlated with fuel-system and 5 other fieldsHigh correlation
engine-type is highly correlated with num-of-cylinders and 1 other fieldsHigh correlation
engine-location is highly correlated with makeHigh correlation
body-style is highly correlated with num-of-doorsHigh correlation
num-of-doors is highly correlated with body-styleHigh correlation
drive-wheels is highly correlated with makeHigh correlation
price_binned is highly correlated with num-of-cylinders and 1 other fieldsHigh correlation
aspiration is highly correlated with fuel-systemHigh correlation
symboling is highly correlated with normalized-losses and 15 other fieldsHigh correlation
normalized-losses is highly correlated with symboling and 14 other fieldsHigh correlation
make is highly correlated with symboling and 28 other fieldsHigh correlation
aspiration is highly correlated with make and 4 other fieldsHigh correlation
num-of-doors is highly correlated with symboling and 4 other fieldsHigh correlation
body-style is highly correlated with make and 8 other fieldsHigh correlation
drive-wheels is highly correlated with symboling and 22 other fieldsHigh correlation
engine-location is highly correlated with make and 7 other fieldsHigh correlation
wheel-base is highly correlated with symboling and 27 other fieldsHigh correlation
length is highly correlated with symboling and 25 other fieldsHigh correlation
width is highly correlated with symboling and 23 other fieldsHigh correlation
height is highly correlated with symboling and 24 other fieldsHigh correlation
curb-weight is highly correlated with symboling and 24 other fieldsHigh correlation
engine-type is highly correlated with symboling and 23 other fieldsHigh correlation
num-of-cylinders is highly correlated with normalized-losses and 22 other fieldsHigh correlation
engine-size is highly correlated with make and 24 other fieldsHigh correlation
fuel-system is highly correlated with make and 22 other fieldsHigh correlation
bore is highly correlated with normalized-losses and 23 other fieldsHigh correlation
stroke is highly correlated with make and 23 other fieldsHigh correlation
compression-ratio is highly correlated with make and 22 other fieldsHigh correlation
horsepower is highly correlated with symboling and 25 other fieldsHigh correlation
peak-rpm is highly correlated with normalized-losses and 19 other fieldsHigh correlation
city-mpg is highly correlated with normalized-losses and 21 other fieldsHigh correlation
highway-mpg is highly correlated with make and 22 other fieldsHigh correlation
price is highly correlated with make and 22 other fieldsHigh correlation
city-L/100km is highly correlated with normalized-losses and 25 other fieldsHigh correlation
highway-L/100km is highly correlated with symboling and 23 other fieldsHigh correlation
price_binned is highly correlated with symboling and 20 other fieldsHigh correlation
diesel is highly correlated with aspiration and 4 other fieldsHigh correlation
gas is highly correlated with aspiration and 4 other fieldsHigh correlation
normalized_length is highly correlated with symboling and 25 other fieldsHigh correlation
normalized_width is highly correlated with symboling and 23 other fieldsHigh correlation
normalized_height is highly correlated with symboling and 24 other fieldsHigh correlation
normalized-losses has 37 (18.4%) missing values Missing
bore has 4 (2.0%) missing values Missing
stroke has 4 (2.0%) missing values Missing
symboling has 65 (32.3%) zeros Zeros

Reproduction

Analysis started2021-10-05 03:25:08.828469
Analysis finished2021-10-05 03:25:40.204348
Duration31.38 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8407960199
Minimum-2
Maximum3
Zeros65
Zeros (%)32.3%
Negative25
Negative (%)12.4%
Memory size1.7 KiB
2021-10-04T23:25:40.245561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.254801723
Coefficient of variation (CV)1.492397315
Kurtosis-0.7071776172
Mean0.8407960199
Median Absolute Deviation (MAD)1
Skewness0.1973703603
Sum169
Variance1.574527363
MonotonicityNot monotonic
2021-10-04T23:25:40.301522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
065
32.3%
152
25.9%
232
15.9%
327
13.4%
-122
 
10.9%
-23
 
1.5%
ValueCountFrequency (%)
-23
 
1.5%
-122
 
10.9%
065
32.3%
152
25.9%
232
15.9%
327
13.4%
ValueCountFrequency (%)
327
13.4%
232
15.9%
152
25.9%
065
32.3%
-122
 
10.9%
-23
 
1.5%

normalized-losses
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct51
Distinct (%)31.1%
Missing37
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean122
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:40.368268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile74
Q194
median115
Q3150
95-th percentile188
Maximum256
Range191
Interquartile range (IQR)56

Descriptive statistics

Standard deviation35.44216753
Coefficient of variation (CV)0.2905095699
Kurtosis0.5254403856
Mean122
Median Absolute Deviation (MAD)24
Skewness0.7659764176
Sum20008
Variance1256.147239
MonotonicityNot monotonic
2021-10-04T23:25:40.445928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16111
 
5.5%
918
 
4.0%
1507
 
3.5%
1346
 
3.0%
1046
 
3.0%
1286
 
3.0%
945
 
2.5%
655
 
2.5%
745
 
2.5%
1025
 
2.5%
Other values (41)100
49.8%
(Missing)37
 
18.4%
ValueCountFrequency (%)
655
2.5%
745
2.5%
771
 
0.5%
781
 
0.5%
812
 
1.0%
833
1.5%
855
2.5%
872
 
1.0%
892
 
1.0%
901
 
0.5%
ValueCountFrequency (%)
2561
 
0.5%
2311
 
0.5%
1972
 
1.0%
1942
 
1.0%
1922
 
1.0%
1882
 
1.0%
1861
 
0.5%
1685
2.5%
1642
 
1.0%
16111
5.5%

make
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
108 

Length

Max length13
Median length6
Mean length6.502487562
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota32
15.9%
nissan18
 
9.0%
mazda17
 
8.5%
mitsubishi13
 
6.5%
honda13
 
6.5%
subaru12
 
6.0%
volkswagen12
 
6.0%
peugot11
 
5.5%
volvo11
 
5.5%
dodge9
 
4.5%
Other values (12)53
26.4%

Length

2021-10-04T23:25:40.523120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota32
15.9%
nissan18
 
9.0%
mazda17
 
8.5%
mitsubishi13
 
6.5%
honda13
 
6.5%
subaru12
 
6.0%
volkswagen12
 
6.0%
peugot11
 
5.5%
volvo11
 
5.5%
dodge9
 
4.5%
Other values (12)53
26.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aspiration
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
165 
turbo
36 

Length

Max length5
Median length3
Mean length3.358208955
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std165
82.1%
turbo36
 
17.9%

Length

2021-10-04T23:25:40.590437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:40.637076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
std165
82.1%
turbo36
 
17.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num-of-doors
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.0%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
four
113 
two
86 

Length

Max length4
Median length4
Mean length3.567839196
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four113
56.2%
two86
42.8%
(Missing)2
 
1.0%

Length

2021-10-04T23:25:40.679880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:40.718749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
four113
56.8%
two86
43.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

body-style
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sedan
94 
hatchback
68 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.611940299
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan94
46.8%
hatchback68
33.8%
wagon25
 
12.4%
hardtop8
 
4.0%
convertible6
 
3.0%

Length

2021-10-04T23:25:40.763065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:40.805702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sedan94
46.8%
hatchback68
33.8%
wagon25
 
12.4%
hardtop8
 
4.0%
convertible6
 
3.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

drive-wheels
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
fwd
118 
rwd
75 
4wd
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd118
58.7%
rwd75
37.3%
4wd8
 
4.0%

Length

2021-10-04T23:25:40.861533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:40.901417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
fwd118
58.7%
rwd75
37.3%
4wd8
 
4.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

engine-location
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
198 
rear
 
3

Length

Max length5
Median length5
Mean length4.985074627
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front198
98.5%
rear3
 
1.5%

Length

2021-10-04T23:25:40.951163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:40.992562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
front198
98.5%
rear3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

wheel-base
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.79701493
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:41.042142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.066365555
Coefficient of variation (CV)0.06140231625
Kurtosis0.9484450961
Mean98.79701493
Median Absolute Deviation (MAD)2.8
Skewness1.031261443
Sum19858.2
Variance36.80079104
MonotonicityNot monotonic
2021-10-04T23:25:41.120264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.720
 
10.0%
94.519
 
9.5%
95.713
 
6.5%
96.58
 
4.0%
97.37
 
3.5%
100.46
 
3.0%
104.36
 
3.0%
96.36
 
3.0%
98.86
 
3.0%
98.46
 
3.0%
Other values (42)104
51.7%
ValueCountFrequency (%)
86.62
 
1.0%
88.41
 
0.5%
88.62
 
1.0%
89.53
 
1.5%
91.32
 
1.0%
931
 
0.5%
93.15
 
2.5%
93.31
 
0.5%
93.720
10.0%
94.31
 
0.5%
ValueCountFrequency (%)
120.91
 
0.5%
115.62
 
1.0%
114.24
2.0%
1132
 
1.0%
1121
 
0.5%
1103
1.5%
109.15
2.5%
1081
 
0.5%
107.96
3.0%
106.71
 
0.5%

length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.200995
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:41.202753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.3
Q1166.8
median173.2
Q3183.5
95-th percentile197
Maximum208.1
Range67
Interquartile range (IQR)16.7

Descriptive statistics

Standard deviation12.32217509
Coefficient of variation (CV)0.07073538868
Kurtosis-0.06519162777
Mean174.200995
Median Absolute Deviation (MAD)6.9
Skewness0.1544463518
Sum35014.4
Variance151.835999
MonotonicityNot monotonic
2021-10-04T23:25:41.281883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.315
 
7.5%
188.811
 
5.5%
186.77
 
3.5%
166.37
 
3.5%
171.77
 
3.5%
186.66
 
3.0%
165.36
 
3.0%
176.26
 
3.0%
177.86
 
3.0%
176.85
 
2.5%
Other values (63)125
62.2%
ValueCountFrequency (%)
141.11
 
0.5%
144.62
 
1.0%
1503
 
1.5%
155.91
 
0.5%
156.91
 
0.5%
157.11
 
0.5%
157.315
7.5%
157.91
 
0.5%
158.73
 
1.5%
158.81
 
0.5%
ValueCountFrequency (%)
208.11
 
0.5%
202.62
1.0%
199.62
1.0%
199.21
 
0.5%
198.94
2.0%
1971
 
0.5%
193.81
 
0.5%
192.73
1.5%
191.71
 
0.5%
190.92
1.0%

width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.88905473
Minimum60.3
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:41.362737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.6
95-th percentile70.3
Maximum72
Range11.7
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.101470819
Coefficient of variation (CV)0.03189408055
Kurtosis0.6786551692
Mean65.88905473
Median Absolute Deviation (MAD)1.4
Skewness0.8750290419
Sum13243.7
Variance4.416179602
MonotonicityNot monotonic
2021-10-04T23:25:41.441296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
63.824
 
11.9%
66.523
 
11.4%
65.415
 
7.5%
68.410
 
5.0%
64.410
 
5.0%
63.69
 
4.5%
649
 
4.5%
65.58
 
4.0%
65.27
 
3.5%
64.26
 
3.0%
Other values (33)80
39.8%
ValueCountFrequency (%)
60.31
 
0.5%
61.81
 
0.5%
62.51
 
0.5%
63.41
 
0.5%
63.69
 
4.5%
63.824
11.9%
63.93
 
1.5%
649
 
4.5%
64.12
 
1.0%
64.26
 
3.0%
ValueCountFrequency (%)
721
 
0.5%
71.73
1.5%
71.43
1.5%
70.91
 
0.5%
70.61
 
0.5%
70.51
 
0.5%
70.33
1.5%
69.62
1.0%
68.94
2.0%
68.81
 
0.5%

height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.76666667
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:41.515661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.447822161
Coefficient of variation (CV)0.04552676059
Kurtosis-0.4329081504
Mean53.76666667
Median Absolute Deviation (MAD)1.6
Skewness0.02917329915
Sum10807.1
Variance5.991833333
MonotonicityNot monotonic
2021-10-04T23:25:41.598349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.814
 
7.0%
55.712
 
6.0%
54.510
 
5.0%
54.110
 
5.0%
529
 
4.5%
55.59
 
4.5%
56.78
 
4.0%
54.38
 
4.0%
52.67
 
3.5%
56.17
 
3.5%
Other values (39)107
53.2%
ValueCountFrequency (%)
47.81
 
0.5%
48.82
 
1.0%
49.42
 
1.0%
49.64
 
2.0%
49.73
 
1.5%
50.26
3.0%
50.51
 
0.5%
50.65
 
2.5%
50.814
7.0%
511
 
0.5%
ValueCountFrequency (%)
59.82
 
1.0%
59.13
 
1.5%
58.74
2.0%
58.31
 
0.5%
57.53
 
1.5%
56.78
4.0%
56.52
 
1.0%
56.32
 
1.0%
56.23
 
1.5%
56.17
3.5%

curb-weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct169
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.666667
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:41.676274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1905
Q12169
median2414
Q32926
95-th percentile3505
Maximum4066
Range2578
Interquartile range (IQR)757

Descriptive statistics

Standard deviation517.2967266
Coefficient of variation (CV)0.2024116577
Kurtosis0.03491557605
Mean2555.666667
Median Absolute Deviation (MAD)377
Skewness0.7058035875
Sum513689
Variance267595.9033
MonotonicityNot monotonic
2021-10-04T23:25:41.757720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23854
 
2.0%
19893
 
1.5%
22753
 
1.5%
19183
 
1.5%
27562
 
1.0%
24142
 
1.0%
24032
 
1.0%
40662
 
1.0%
21452
 
1.0%
23372
 
1.0%
Other values (159)176
87.6%
ValueCountFrequency (%)
14881
0.5%
17131
0.5%
18191
0.5%
18371
0.5%
18741
0.5%
18762
1.0%
18891
0.5%
18901
0.5%
19001
0.5%
19051
0.5%
ValueCountFrequency (%)
40662
1.0%
39501
0.5%
39001
0.5%
37701
0.5%
37501
0.5%
37401
0.5%
37151
0.5%
36851
0.5%
35151
0.5%
35051
0.5%

engine-type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
145 
ohcf
15 
ohcv
 
13
l
 
12
dohc
 
12

Length

Max length5
Median length3
Mean length3.119402985
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc145
72.1%
ohcf15
 
7.5%
ohcv13
 
6.5%
l12
 
6.0%
dohc12
 
6.0%
rotor4
 
2.0%

Length

2021-10-04T23:25:41.835570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:41.883568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
ohc145
72.1%
ohcf15
 
7.5%
ohcv13
 
6.5%
l12
 
6.0%
dohc12
 
6.0%
rotor4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num-of-cylinders
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
157 
six
24 
five
 
10
two
 
4
eight
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.895522388
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four157
78.1%
six24
 
11.9%
five10
 
5.0%
two4
 
2.0%
eight4
 
2.0%
twelve1
 
0.5%
three1
 
0.5%

Length

2021-10-04T23:25:41.945847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:41.992440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
four157
78.1%
six24
 
11.9%
five10
 
5.0%
two4
 
2.0%
eight4
 
2.0%
twelve1
 
0.5%
three1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

engine-size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.8756219
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:42.055922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q198
median120
Q3141
95-th percentile194
Maximum326
Range265
Interquartile range (IQR)43

Descriptive statistics

Standard deviation41.54683445
Coefficient of variation (CV)0.3274611295
Kurtosis5.497490767
Mean126.8756219
Median Absolute Deviation (MAD)22
Skewness1.979144197
Sum25502
Variance1726.139453
MonotonicityNot monotonic
2021-10-04T23:25:42.131042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
12215
 
7.5%
9215
 
7.5%
9714
 
7.0%
9814
 
7.0%
10813
 
6.5%
11012
 
6.0%
9010
 
5.0%
1098
 
4.0%
1417
 
3.5%
1207
 
3.5%
Other values (33)86
42.8%
ValueCountFrequency (%)
611
 
0.5%
703
 
1.5%
791
 
0.5%
801
 
0.5%
9010
5.0%
915
 
2.5%
9215
7.5%
9714
7.0%
9814
7.0%
1031
 
0.5%
ValueCountFrequency (%)
3261
 
0.5%
3081
 
0.5%
3041
 
0.5%
2582
 
1.0%
2342
 
1.0%
2093
1.5%
1943
1.5%
1834
2.0%
1816
3.0%
1731
 
0.5%

fuel-system
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
92 
2bbl
64 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.895522388
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi92
45.8%
2bbl64
31.8%
idi20
 
10.0%
1bbl11
 
5.5%
spdi9
 
4.5%
4bbl3
 
1.5%
spfi1
 
0.5%
mfi1
 
0.5%

Length

2021-10-04T23:25:42.201574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:42.246982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mpfi92
45.8%
2bbl64
31.8%
idi20
 
10.0%
1bbl11
 
5.5%
spdi9
 
4.5%
4bbl3
 
1.5%
spfi1
 
0.5%
mfi1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

bore
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)19.3%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.33071066
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:42.315869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.96
Q13.15
median3.31
Q33.59
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.2707934346
Coefficient of variation (CV)0.08130199896
Kurtosis-0.8427208688
Mean3.33071066
Median Absolute Deviation (MAD)0.26
Skewness-0.03262168703
Sum656.15
Variance0.07332908422
MonotonicityNot monotonic
2021-10-04T23:25:42.386727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.6223
 
11.4%
3.1920
 
10.0%
3.1515
 
7.5%
2.9712
 
6.0%
3.0310
 
5.0%
3.469
 
4.5%
3.788
 
4.0%
3.318
 
4.0%
3.438
 
4.0%
2.917
 
3.5%
Other values (28)77
38.3%
ValueCountFrequency (%)
2.541
 
0.5%
2.681
 
0.5%
2.917
3.5%
2.921
 
0.5%
2.9712
6.0%
2.991
 
0.5%
3.015
2.5%
3.0310
5.0%
3.056
3.0%
3.081
 
0.5%
ValueCountFrequency (%)
3.941
 
0.5%
3.82
 
1.0%
3.788
 
4.0%
3.761
 
0.5%
3.743
 
1.5%
3.75
 
2.5%
3.632
 
1.0%
3.6223
11.4%
3.611
 
0.5%
3.61
 
0.5%

stroke
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct36
Distinct (%)18.3%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.256903553
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:42.462022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31925624
Coefficient of variation (CV)0.09802446857
Kurtosis2.028784197
Mean3.256903553
Median Absolute Deviation (MAD)0.17
Skewness-0.6937783864
Sum641.61
Variance0.1019245468
MonotonicityNot monotonic
2021-10-04T23:25:42.530707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.419
 
9.5%
3.1514
 
7.0%
3.0314
 
7.0%
3.2314
 
7.0%
3.3913
 
6.5%
2.6411
 
5.5%
3.359
 
4.5%
3.299
 
4.5%
3.468
 
4.0%
3.56
 
3.0%
Other values (26)80
39.8%
ValueCountFrequency (%)
2.071
 
0.5%
2.192
 
1.0%
2.361
 
0.5%
2.6411
5.5%
2.682
 
1.0%
2.761
 
0.5%
2.82
 
1.0%
2.871
 
0.5%
2.93
 
1.5%
3.0314
7.0%
ValueCountFrequency (%)
4.172
 
1.0%
3.93
 
1.5%
3.864
2.0%
3.645
2.5%
3.586
3.0%
3.544
2.0%
3.525
2.5%
3.56
3.0%
3.474
2.0%
3.468
4.0%

compression-ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.16427861
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:42.596463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.9
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation4.004965493
Coefficient of variation (CV)0.3940235848
Kurtosis5.068872476
Mean10.16427861
Median Absolute Deviation (MAD)0.4
Skewness2.584462433
Sum2043.02
Variance16.0397486
MonotonicityNot monotonic
2021-10-04T23:25:42.660028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
946
22.9%
9.426
12.9%
8.514
 
7.0%
9.513
 
6.5%
9.311
 
5.5%
8.79
 
4.5%
88
 
4.0%
9.28
 
4.0%
76
 
3.0%
235
 
2.5%
Other values (22)55
27.4%
ValueCountFrequency (%)
76
3.0%
7.55
 
2.5%
7.64
 
2.0%
7.72
 
1.0%
7.81
 
0.5%
88
4.0%
8.12
 
1.0%
8.33
 
1.5%
8.45
 
2.5%
8.514
7.0%
ValueCountFrequency (%)
235
2.5%
22.71
 
0.5%
22.53
1.5%
221
 
0.5%
21.91
 
0.5%
21.54
2.0%
215
2.5%
11.51
 
0.5%
10.11
 
0.5%
102
 
1.0%

horsepower
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)29.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean103.3969849
Minimum48
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:42.736194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile176.6
Maximum262
Range214
Interquartile range (IQR)46

Descriptive statistics

Standard deviation37.55384271
Coefficient of variation (CV)0.3632005588
Kurtosis1.27867076
Mean103.3969849
Median Absolute Deviation (MAD)25
Skewness1.141584284
Sum20576
Variance1410.291102
MonotonicityNot monotonic
2021-10-04T23:25:42.810699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6819
 
9.5%
6910
 
5.0%
709
 
4.5%
1169
 
4.5%
1108
 
4.0%
957
 
3.5%
886
 
3.0%
1016
 
3.0%
626
 
3.0%
1146
 
3.0%
Other values (48)113
56.2%
ValueCountFrequency (%)
481
 
0.5%
522
 
1.0%
551
 
0.5%
562
 
1.0%
581
 
0.5%
601
 
0.5%
626
 
3.0%
641
 
0.5%
6819
9.5%
6910
5.0%
ValueCountFrequency (%)
2621
 
0.5%
2073
1.5%
2001
 
0.5%
1842
 
1.0%
1823
1.5%
1762
 
1.0%
1751
 
0.5%
1622
 
1.0%
1612
 
1.0%
1605
2.5%

peak-rpm
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)11.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean5117.58794
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:43.145831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4245
Q14800
median5200
Q35500
95-th percentile6000
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation480.5218239
Coefficient of variation (CV)0.09389615374
Kurtosis0.07658783227
Mean5117.58794
Median Absolute Deviation (MAD)300
Skewness0.1077292866
Sum1018400
Variance230901.2233
MonotonicityNot monotonic
2021-10-04T23:25:43.209485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
550036
17.9%
480036
17.9%
500027
13.4%
520023
11.4%
540011
 
5.5%
60009
 
4.5%
52507
 
3.5%
45007
 
3.5%
58007
 
3.5%
41505
 
2.5%
Other values (12)31
15.4%
ValueCountFrequency (%)
41505
 
2.5%
42005
 
2.5%
42503
 
1.5%
43504
 
2.0%
44003
 
1.5%
45007
 
3.5%
46501
 
0.5%
47504
 
2.0%
480036
17.9%
49001
 
0.5%
ValueCountFrequency (%)
66002
 
1.0%
60009
 
4.5%
59003
 
1.5%
58007
 
3.5%
56001
 
0.5%
550036
17.9%
540011
 
5.5%
53001
 
0.5%
52507
 
3.5%
520023
11.4%

city-mpg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.17910448
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:43.273890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.423220469
Coefficient of variation (CV)0.2551012279
Kurtosis0.7539680878
Mean25.17910448
Median Absolute Deviation (MAD)5
Skewness0.6804334707
Sum5061
Variance41.25776119
MonotonicityNot monotonic
2021-10-04T23:25:43.340704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3128
13.9%
1927
13.4%
2422
10.9%
2714
 
7.0%
1712
 
6.0%
2312
 
6.0%
2612
 
6.0%
218
 
4.0%
258
 
4.0%
308
 
4.0%
Other values (19)50
24.9%
ValueCountFrequency (%)
131
 
0.5%
142
 
1.0%
153
 
1.5%
165
 
2.5%
1712
6.0%
183
 
1.5%
1927
13.4%
203
 
1.5%
218
 
4.0%
224
 
2.0%
ValueCountFrequency (%)
491
 
0.5%
471
 
0.5%
451
 
0.5%
385
2.5%
376
3.0%
361
 
0.5%
351
 
0.5%
341
 
0.5%
331
 
0.5%
321
 
0.5%

highway-mpg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.68656716
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:43.411381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.815149936
Coefficient of variation (CV)0.22208903
Kurtosis0.5611711398
Mean30.68656716
Median Absolute Deviation (MAD)5
Skewness0.5495071459
Sum6168
Variance46.44626866
MonotonicityNot monotonic
2021-10-04T23:25:43.478520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2519
 
9.5%
3817
 
8.5%
2417
 
8.5%
3016
 
8.0%
3216
 
8.0%
3414
 
7.0%
3713
 
6.5%
2812
 
6.0%
2910
 
5.0%
339
 
4.5%
Other values (20)58
28.9%
ValueCountFrequency (%)
162
 
1.0%
171
 
0.5%
182
 
1.0%
192
 
1.0%
202
 
1.0%
227
 
3.5%
237
 
3.5%
2417
8.5%
2519
9.5%
263
 
1.5%
ValueCountFrequency (%)
541
 
0.5%
531
 
0.5%
501
 
0.5%
472
 
1.0%
462
 
1.0%
432
 
1.0%
423
 
1.5%
413
 
1.5%
392
 
1.0%
3817
8.5%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct186
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13207.12935
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:43.554871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6189
Q17775
median10295
Q316500
95-th percentile32528
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation7947.066342
Coefficient of variation (CV)0.601725487
Kurtosis3.231536887
Mean13207.12935
Median Absolute Deviation (MAD)3306
Skewness1.809675339
Sum2654633
Variance63155863.44
MonotonicityNot monotonic
2021-10-04T23:25:43.631029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134992
 
1.0%
165002
 
1.0%
92792
 
1.0%
76092
 
1.0%
62292
 
1.0%
84952
 
1.0%
181502
 
1.0%
89212
 
1.0%
78982
 
1.0%
66922
 
1.0%
Other values (176)181
90.0%
ValueCountFrequency (%)
51181
0.5%
51511
0.5%
51951
0.5%
53481
0.5%
53891
0.5%
53991
0.5%
54991
0.5%
55722
1.0%
60951
0.5%
61891
0.5%
ValueCountFrequency (%)
454001
0.5%
413151
0.5%
409601
0.5%
370281
0.5%
368801
0.5%
360001
0.5%
355501
0.5%
350561
0.5%
341841
0.5%
340281
0.5%

city-L/100km
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.944145484
Minimum4.795918367
Maximum18.07692308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:43.704214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4.795918367
5-th percentile6.351351351
Q17.833333333
median9.791666667
Q312.36842105
95-th percentile14.6875
Maximum18.07692308
Range13.28100471
Interquartile range (IQR)4.535087719

Descriptive statistics

Standard deviation2.534599261
Coefficient of variation (CV)0.254883566
Kurtosis-0.06511888838
Mean9.944145484
Median Absolute Deviation (MAD)1.958333333
Skewness0.5923833561
Sum1998.773242
Variance6.424193415
MonotonicityNot monotonic
2021-10-04T23:25:43.776283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
7.58064516128
13.9%
12.3684210527
13.4%
9.79166666722
10.9%
8.70370370414
 
7.0%
9.03846153812
 
6.0%
10.217391312
 
6.0%
13.8235294112
 
6.0%
11.190476198
 
4.0%
7.8333333338
 
4.0%
9.48
 
4.0%
Other values (19)50
24.9%
ValueCountFrequency (%)
4.7959183671
 
0.5%
51
 
0.5%
5.2222222221
 
0.5%
6.1842105265
2.5%
6.3513513516
3.0%
6.5277777781
 
0.5%
6.7142857141
 
0.5%
6.9117647061
 
0.5%
7.1212121211
 
0.5%
7.343751
 
0.5%
ValueCountFrequency (%)
18.076923081
 
0.5%
16.785714292
 
1.0%
15.666666673
 
1.5%
14.68755
 
2.5%
13.8235294112
6.0%
13.055555563
 
1.5%
12.3684210527
13.4%
11.753
 
1.5%
11.190476198
 
4.0%
10.681818184
 
2.0%

highway-L/100km
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.044956825
Minimum4.351851852
Maximum14.6875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2021-10-04T23:25:43.845573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4.351851852
5-th percentile5.595238095
Q16.911764706
median7.833333333
Q39.4
95-th percentile10.68181818
Maximum14.6875
Range10.33564815
Interquartile range (IQR)2.488235294

Descriptive statistics

Standard deviation1.840738522
Coefficient of variation (CV)0.2288065135
Kurtosis1.235190109
Mean8.044956825
Median Absolute Deviation (MAD)1.481981982
Skewness0.8496621786
Sum1617.036322
Variance3.388318308
MonotonicityNot monotonic
2021-10-04T23:25:43.913862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9.419
 
9.5%
9.79166666717
 
8.5%
6.18421052617
 
8.5%
7.3437516
 
8.0%
7.83333333316
 
8.0%
6.91176470614
 
7.0%
6.35135135113
 
6.5%
8.39285714312
 
6.0%
8.10344827610
 
5.0%
7.1212121219
 
4.5%
Other values (20)58
28.9%
ValueCountFrequency (%)
4.3518518521
 
0.5%
4.4339622641
 
0.5%
4.71
 
0.5%
52
 
1.0%
5.1086956522
 
1.0%
5.4651162792
 
1.0%
5.5952380953
 
1.5%
5.7317073173
 
1.5%
6.0256410262
 
1.0%
6.18421052617
8.5%
ValueCountFrequency (%)
14.68752
 
1.0%
13.823529411
 
0.5%
13.055555562
 
1.0%
12.368421052
 
1.0%
11.752
 
1.0%
10.681818187
 
3.5%
10.21739137
 
3.5%
9.79166666717
8.5%
9.419
9.5%
9.0384615383
 
1.5%

price_binned
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Low
171 
Median
18 
High
 
12

Length

Max length6
Median length3
Mean length3.328358209
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Low171
85.1%
Median18
 
9.0%
High12
 
6.0%

Length

2021-10-04T23:25:43.984233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:44.029379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
low171
85.1%
median18
 
9.0%
high12
 
6.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diesel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
181 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0181
90.0%
120
 
10.0%

Length

2021-10-04T23:25:44.077255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:44.116770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0181
90.0%
120
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

gas
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
181 
0
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1181
90.0%
020
 
10.0%

Length

2021-10-04T23:25:44.160402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-04T23:25:44.200333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1181
90.0%
020
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

normalized_length
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.628106163 × 10-14
Minimum-2.686294812
Maximum2.751056913
Zeros0
Zeros (%)0.0%
Negative110
Negative (%)54.7%
Memory size1.7 KiB
2021-10-04T23:25:44.246875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.686294812
5-th percentile-1.371591858
Q1-0.6006240757
median-0.08123525412
Q30.7546561306
95-th percentile1.850241926
Maximum2.751056913
Range5.437351726
Interquartile range (IQR)1.355280206

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-6.142105612 × 1013
Kurtosis-0.06519162777
Mean-1.628106163 × 10-14
Median Absolute Deviation (MAD)0.5599660732
Skewness0.1544463518
Sum-3.27116112 × 10-12
Variance1
MonotonicityNot monotonic
2021-10-04T23:25:44.329440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.37159185815
 
7.5%
1.18477499811
 
5.5%
1.0143505417
 
3.5%
-0.64120132737
 
3.5%
-0.20296700927
 
3.5%
0.29207546146
 
3.0%
-0.72235583076
 
3.0%
1.0062350916
 
3.0%
0.1622282566
 
3.0%
0.1135355545
 
2.5%
Other values (63)125
62.2%
ValueCountFrequency (%)
-2.6862948121
 
0.5%
-2.402254052
 
1.0%
-1.9640197323
 
1.5%
-1.4852081621
 
0.5%
-1.4040536591
 
0.5%
-1.3878227581
 
0.5%
-1.37159185815
7.5%
-1.3228991561
 
0.5%
-1.2579755533
 
1.5%
-1.2498601031
 
0.5%
ValueCountFrequency (%)
2.7510569131
 
0.5%
2.3047071452
1.0%
2.0612436352
1.0%
2.0287818331
 
0.5%
2.0044354824
2.0%
1.8502419261
 
0.5%
1.5905475151
 
0.5%
1.5012775623
1.5%
1.4201230581
 
0.5%
1.3551994552
1.0%

normalized_width
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.443621342 × 10-14
Minimum-2.65959188
Maximum2.90793725
Zeros0
Zeros (%)0.0%
Negative116
Negative (%)57.7%
Memory size1.7 KiB
2021-10-04T23:25:44.404308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.65959188
5-th percentile-1.089263151
Q1-0.851334556
median-0.1851344891
Q30.3383084206
95-th percentile2.098980026
Maximum2.90793725
Range5.56752913
Interquartile range (IQR)1.189642977

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)6.92702422 × 1013
Kurtosis0.6786551692
Mean1.443621342 × 10-14
Median Absolute Deviation (MAD)0.6662000669
Skewness0.8750290419
Sum2.899014362 × 10-12
Variance1
MonotonicityNot monotonic
2021-10-04T23:25:44.473985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-0.994091713224
 
11.9%
0.290722701523
 
11.4%
-0.232720208215
 
7.5%
-0.708577398810
 
5.0%
1.19485136410
 
5.0%
-0.8989202759
 
4.5%
-1.0892631519
 
4.5%
-0.18513448918
 
4.0%
-0.32789164637
 
3.5%
0.6238227356
 
3.0%
Other values (33)80
39.8%
ValueCountFrequency (%)
-2.659591881
 
0.5%
-1.9458060941
 
0.5%
-1.6127060611
 
0.5%
-1.1844345891
 
0.5%
-1.0892631519
 
4.5%
-0.994091713224
11.9%
-0.94650599413
 
1.5%
-0.8989202759
 
4.5%
-0.8513345562
 
1.0%
-0.80374883696
 
3.0%
ValueCountFrequency (%)
2.907937251
 
0.5%
2.7651800933
1.5%
2.6224229363
1.5%
2.384494341
 
0.5%
2.2417371831
 
0.5%
2.1941514641
 
0.5%
2.0989800263
1.5%
1.7658799932
1.0%
1.4327799594
2.0%
1.385194241
 
0.5%

normalized_height
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.980560547 × 10-14
Minimum-2.437540913
Maximum2.464776007
Zeros0
Zeros (%)0.0%
Negative94
Negative (%)46.8%
Memory size1.7 KiB
2021-10-04T23:25:44.548740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.437540913
5-th percentile-1.661340734
Q1-0.7217299911
median0.13617547
Q30.7081124441
95-th percentile1.525165264
Maximum2.464776007
Range4.902316921
Interquartile range (IQR)1.429842435

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-5.049075634 × 1013
Kurtosis-0.4329081504
Mean-1.980560547 × 10-14
Median Absolute Deviation (MAD)0.6536422561
Skewness0.02917329915
Sum-3.979150343 × 10-12
Variance1
MonotonicityNot monotonic
2021-10-04T23:25:44.625138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-1.21196168314
 
7.0%
0.789817726112
 
6.0%
0.1361754710
 
5.0%
0.29958603410
 
5.0%
0.70811244419
 
4.5%
-0.72172999119
 
4.5%
0.2178807528
 
4.0%
1.1983441368
 
4.0%
-0.88514055517
 
3.5%
0.95322829017
 
3.5%
Other values (39)107
53.2%
ValueCountFrequency (%)
-2.4375409131
 
0.5%
-2.0290145032
 
1.0%
-1.7838986572
 
1.0%
-1.7021933754
 
2.0%
-1.6613407343
 
1.5%
-1.4570775296
3.0%
-1.3345196061
 
0.5%
-1.2936669655
 
2.5%
-1.21196168314
7.0%
-1.1302564011
 
0.5%
ValueCountFrequency (%)
2.4647760072
 
1.0%
2.178807523
 
1.5%
2.0153969564
2.0%
1.8519863921
 
0.5%
1.5251652643
 
1.5%
1.1983441368
4.0%
1.1166388542
 
1.0%
1.0349335722
 
1.0%
0.99408093113
 
1.5%
0.95322829017
3.5%

Interactions

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2021-10-04T23:25:37.724266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-04T23:25:39.005361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-04T23:25:12.462800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-04T23:25:35.019538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-04T23:25:36.286792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-04T23:25:25.968270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-04T23:25:37.841504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-04T23:25:44.713428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-04T23:25:44.883829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-04T23:25:45.040523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-04T23:25:45.186464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-04T23:25:45.314861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-04T23:25:39.217000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-04T23:25:39.822838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-04T23:25:39.982605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-04T23:25:40.088151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

symbolingnormalized-lossesmakeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgpricecity-L/100kmhighway-L/100kmprice_binneddieselgasnormalized_lengthnormalized_widthnormalized_height
03NaNalfa-romerostdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.021271349511.1904768.703704Low01-0.438315-0.851335-2.029015
13NaNalfa-romerostdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.021271650011.1904768.703704Low01-0.438315-0.851335-2.029015
21NaNalfa-romerostdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.0154.05000.019261650012.3684219.038462Low01-0.243544-0.185134-0.558319
32164.0audistdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.0102.05500.02430139509.7916677.833333Low010.1946900.1479660.217881
42164.0audistdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.0115.05500.018221745013.05555610.681818Low010.1946900.2431370.217881
52NaNaudistdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.5110.05500.019251525012.3684219.400000Low010.2514980.195551-0.272351
61158.0audistdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.5110.05500.019251771012.3684219.400000Low011.5012782.6224230.789818
71NaNaudistdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.5110.05500.019251892012.3684219.400000Median011.5012782.6224230.789818
81158.0auditurbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.3140.05500.017202387513.82352911.750000Median011.5012782.6224230.871523
92192.0bmwstdtwosedanrwdfront101.2176.864.854.32395ohcfour108mpfi3.502.808.8101.05800.023291643010.2173918.103448Low010.210921-0.5182350.217881

Last rows

symbolingnormalized-lossesmakeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgpricecity-L/100kmhighway-L/100kmprice_binneddieselgasnormalized_lengthnormalized_widthnormalized_height
191-174.0volvostdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.5114.05400.023281341510.2173918.392857Low011.1847750.6238231.525165
192-2103.0volvostdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.5114.05400.02428159859.7916678.392857Low011.1847750.6238230.994081
193-174.0volvostdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.5114.05400.02428165159.7916678.392857Low011.1847750.6238231.525165
194-2103.0volvoturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.5162.05100.017221842013.82352910.681818Low011.1847750.6238230.994081
195-174.0volvoturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.5162.05100.017221895013.82352910.681818Median011.1847750.6238231.525165
196-195.0volvostdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.5114.05400.023281684510.2173918.392857Low011.1847751.4327800.708112
197-195.0volvoturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.7160.05300.019251904512.3684219.400000Median011.1847751.3851940.708112
198-195.0volvostdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.8134.05500.018232148513.05555610.217391Median011.1847751.4327800.708112
199-195.0volvoturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.0106.04800.02627224709.0384628.703704Median101.1847751.4327800.708112
200-195.0volvoturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.5114.05400.019252262512.3684219.400000Median011.1847751.4327800.708112